Browse > Article
http://dx.doi.org/10.9708/jksci.2011.16.2.025

A Variant of Improved Robust Fuzzy PCA  

Kim, Seong-Hoon (Division of Computer Information, Kyungpook National Univ.)
Heo, Gyeong-Yong (Visual Media Center, Dong-Eui Univ.)
Woo, Young-Woon (Dept. of Multimedia Eng., Dong-Eui Univ.)
Publication Information
Journal of the Korea Society of Computer and Information / v.16, no.2, 2011 , pp. 25-31 More about this Journal
Abstract
Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction. Although PCA has been applied in many areas successfully, it is sensitive to outliers due to the use of sum-square-error. Several variants of PCA have been proposed to resolve the noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA2, however, still can fall into a local optimum due to equal initial membership values for all data points. Another reason comes from the fact that RF-PCA2 is based on sum-square-error although fuzzy memberships are incorporated. In this paper, a variant of RF-PCA2 called RF-PCA3 is proposed. The proposed algorithm is based on the objective function of RF-PCA2. RF-PCA3 augments RF-PCA2 with the objective function of PCA and initial membership calculation using data distribution, which make RF-PCA3 to have more chance to converge on a better solution than that of RF-PCA2. RF-PCA3 outperforms RF-PCA2, which is demonstrated by experimental results.
Keywords
Principal Component Analysis; Noise Sensitivity; Locally Optimal Solution;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 P. Rousseeuw, "Multivariate estimation with high breakdown point," Mathematical Statistics and Applications B, pp. 283-297, Dec. 1985.
2 C. D. Lu, T. Y. Zhang, X. Z. Du, and C. P. Li, "A robust kernel PCA algorithm," Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, pp. 3084-3087, Aug. 2004.
3 C. Lu, T. Zhang, R. Zhang, and C. Zhang, "Adaptive robust kernel PCA algorithm," Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. VI 621-624, Apr. 2003.
4 Seok-Woo Jang, Moon-Haeng Huh, Gye-Young Kim, "Effective Handwriting Verification through DTW and PCA," Journal of the Korea society of computer and information, Vol. 14, No. 7, pp. 25-32, Jul. 2009.
5 G. Heo, P. Gader, and H. Frigui, "RKF-PCA: Robust kernel fuzzy PCA," Neural Networks, Vol. 22, No. 5-6, pp. 642-650, Aug. 2009.   DOI   ScienceOn
6 T. N. Yang and S. D. Wang, "Fuzzy auto-associative neural networks for principal component extraction of noisy data," IEEE Transaction on Neural Networks, Vol. 11, No. 3, pp. 808-810, Mar. 2000.   DOI   ScienceOn
7 T. R. Cundari, C. Sarbu, and H. F. Pop, "Robust fuzzy principal component analysis (FPCA). A comparative study concerning interaction of carbonhydrogen bonds with molybdenum-oxo bonds," Journal of Chemical Information and Computer Sciences, Vol. 42, No. 6, pp. 1363-1369, Dec. 2002.   DOI
8 Gyeongyong Heo, Young Woon Woo and Seong Hoon Kim, "An Improved Robust Fuzzy Principal Component Analysis," The Journal of the Korean Institute of Maritime Information and Communication Sciences, Vol. 14, No. 5, pp. 1093-1102, May 2010.   DOI   ScienceOn
9 G. Heo and P. Gader, "Fuzzy SVM for noisy data: A robust membership calculation method," Proceedings of the 2009 IEEE International Confernce on Fuzzy Systems, pp. 431-436, Aug. 2009.
10 V. J. Hodge and J. Austin, "A Survey of Outlier Detection Methodologies," Artificial Intelligence Review, vol. 22, No. 2, pp. 85-126, Oct. 2004.   DOI
11 I. T. Jolliffe, Principal Component Analysis, 2nd Edition, Springer, 2002.